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Gayatri Putri
Department of Library and Informatics Science, Enigma Institute, Palembang, Indonesia
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Building a Profession from the Ground Up: A Longitudinal Study of Teacher Professional Development and Pedagogical Innovation in Papuan Private Schools Iis Sugandhi; Arya Ganendra; Aaliyah El-Hussaini; Gayatri Putri; Evelyn Wang; Anita Havyasari; Muhammad Hasan
Enigma in Education Vol. 3 No. 1 (2025): Enigma in Education
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/edu.v3i1.90

Abstract

Teacher quality is the most significant school-based determinant of student success, yet fostering professional excellence in remote and culturally diverse regions like Papua, Indonesia, presents profound challenges. Private schools often fill critical educational gaps but their teachers can be professionally isolated. This study addressed the gap in long-term, evidence-based research on teacher professional development (TPD) in this unique context. A three-year (2021-2024) concurrent mixed-methods longitudinal study was conducted. The study involved 50 teachers from a network of five private schools in urban, semi-rural, and remote highland regions of Papua. A comprehensive TPD program, focusing on student-centered learning and culturally responsive pedagogy, was implemented. Quantitative data were collected annually using the Teacher Pedagogical Knowledge Test (TPKT), the Teacher Self-Efficacy Scale (TSES), and a structured Classroom Observation Protocol. Qualitative data were gathered through semi-structured interviews, teacher reflective journals, and focus group discussions with Professional Learning Communities (PLCs). Quantitative data were analyzed using repeated measures ANOVA, while qualitative data were analyzed thematically. The longitudinal quantitative analysis revealed statistically significant improvements across all three years. Mean TPKT scores increased from 48.5 (SD=11.2) at baseline to 79.8 (SD=8.5) at endline (F(2, 98) = 157.2, p <0.001). Teacher self-efficacy scores also showed significant growth (F(2, 98) = 112.9, p <0.001). Classroom observations confirmed a marked shift from teacher-centered to student-centered practices. Qualitative findings identified three core themes: (1) "From Transmission to Facilitation: A Pedagogical Awakening," detailing the shift in teachers' core beliefs about learning; (2) "The Power of the Collective," highlighting the crucial role of PLCs in sustaining motivation and collaborative problem-solving; and (3) "Navigating the Cultural Interface," illustrating the teachers' journey in adapting curriculum to be more culturally responsive. In conclusion, sustained, context-specific, and collaborative TPD can foster profound and lasting improvements in teacher knowledge, self-efficacy, and classroom practice, even in highly challenging environments. The findings advocate for a shift away from isolated, short-term workshops towards integrated, long-term models that prioritize peer collaboration and cultural relevance, revealing a clear pathway from knowledge acquisition to a transformed professional identity.
Effectiveness of an Automated Feedback System on Creative Writing and Scientific Translation Skills Among Undergraduate Students: A Quasi-Experimental Study Danila Adi Sanjaya; Gayatri Putri
Enigma in Education Vol. 4 No. 1 (2026): Enigma in Education
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/edu.v4i1.123

Abstract

This quasi-experimental study investigates the effectiveness of an automated feedback system (AFS) on creative writing skills (CWS), scientific translation skills (STS), and writing self-efficacy (WSE) among undergraduate students. Participants comprised 136 students (experimental group: n = 68; control group: n = 68) from the Department of English Education at a public university in Palembang, Indonesia, enrolled in Writing and Translation courses during a 10-week intervention period. The experimental group received automated feedback through an integrated computer-assisted language learning platform, while the control group received conventional instructor-provided feedback. Pre- and post-test data were analyzed using Analysis of Covariance (ANCOVA), with large effect sizes observed across all dependent variables: creative writing skills (d = 1.84, 95% CI [1.38, 2.30], p < 0.001); scientific translation skills (d = 2.08, 95% CI [1.62, 2.54], p < 0.001); and writing self-efficacy (d = 1.94, 95% CI [1.49, 2.39], p < 0.001). Results demonstrate that automated feedback systems significantly enhance both skill development and learner confidence in academic writing contexts, particularly for skill-intensive tasks requiring iterative practice. The study also underscores the importance of combining automated feedback with periodic instructor guidance and peer collaboration.
Deep learning-assisted digital VIA via telemedicine and HPV self-sampling for CIN2+ detection in remote archipelago women: a prospective diagnostic accuracy study Theresia Putri Sinaga; Nur Diana; Firman Hadi; Gayatri Putri
Sriwijaya Journal of Obstetrics and Gynecology Vol. 4 No. 1 (2026): Sriwijaya Journal of Obstetrics & Gynecology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjog.v4i1.302

Abstract

Background: Cervical cancer disproportionately burdens women in low- and middle-income countries, where archipelagic geography and colposcopist shortages obstruct screening. Human papillomavirus (HPV) self-sampling is highly sensitive but poorly specific, generating colposcopy referrals that exceed remote-area capacity. Objective: To evaluate whether deep-learning (DL)-assisted digital visual inspection with acetic acid (VIA), delivered by telemedicine, can triage HPV-positive women and detect high-grade cervical intraepithelial neoplasia (CIN2+). Methods: In a prospective, double-blind, STARD-compliant diagnostic accuracy study, 642 women aged 30–50 years at an urban tertiary referral hospital (Center A) and five remote community health centers (Region B) in an Indonesian archipelago province underwent HPV-DNA self-sampling and smartphone-captured digital VIA analyzed by a MobileNetV2 convolutional neural network. All participants received colposcopy-directed biopsy as the reference standard, eliminating verification bias. Metrics used Wilson 95% confidence intervals (CI); multivariable logistic regression and decision-analytic triage metrics were derived. Results: CIN2+ prevalence was 13.1% (95% CI 10.7–15.9). DL-assisted VIA achieved sensitivity 91.7% (95% CI 83.8–95.9), specificity 88.4% (85.4–90.8), and AUC 0.93, exceeding human-read VIA (sensitivity 67.9%). HPV self-sampling was most sensitive (95.2%) but least specific (81.5%). A sequential HPV→DL-VIA pathway raised specificity to 95.7% and positive predictive value to 75.5%, reducing colposcopy referrals by 46.4% and unnecessary referrals by 76.7% (number-needed-to-screen 7.6). HPV positivity dominated the multivariable model (adjusted OR 91.5, 95% CI 32.7–256.2, p<0.001; Nagelkerke R² 0.50). Conclusion: Telemedicine-delivered, DL-assisted VIA is an accurate triage for HPV-positive women that conserves scarce colposcopy capacity while preserving CIN2+ detection. This decentralized two-step pathway is a scalable strategy for advancing cervical-cancer elimination in geographically isolated populations.
Zombie Norms in Indonesian Regional Law: Measuring Vertical Disharmony with the Job Creation Law Across Three Regulatory Domains Andi Fatihah Syahrir; Gayatri Putri; Henrietta Noir
Enigma in Law Vol. 3 No. 2 (2025): Enigma in Law
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/law.v3i2.128

Abstract

The Job Creation Law (Law No. 11 of 2020, reconstituted as Law No. 6 of 2023) reorganised Indonesian business licensing, spatial-use control, and regional levies, abolishing a generation of sub-national instruments; yet harmonisation of the regional legislative stock has never been measured across domains. Six binary indicators of disharmony — obsolete legal basis, regulation of an abolished instrument, absence of new-regime terminology, pre-reform enactment still in force, a levy attached to an abolished instrument, and no post-2020 amendment or repeal — were aggregated into a 0–6 index. Eighty regional regulations (Perda) from seventy regions, enacted 2001–2025, were coded from official register metadata, each record carrying its verbatim legal-basis citation so that every coding decision is auditable. Seventy-seven instruments (96.3%) remain in force, at a mean index of 4.29 (SD 1.73). Disharmony is a legacy phenomenon: pre-reform instruments scored 5.22 (SD 0.77) against 1.82 (SD 0.91) post-reform (Welch t(33.2) = 15.60, p < 0.001, d = 4.20), an effect surviving controls for domain and tier (b = −3.24; R² = 0.808), while domains did not differ (H = 5.66, p = 0.059). Decisively, absence of post-2020 legislative action alone failed to discriminate between cohorts (Fisher's p = 1.00): regions draft new law correctly but almost never reopen the stock. Five instruments still rest on the colonial Hinder Ordonnantie of 1926. The failure is one of regulatory stock management, traceable to the removal of executive review of Perda in 2017 without substitution, and calls for statutory sunset rules rather than administrative annulment.
An In-Silico Investigation of Machine Learning for Integrating Genomic and Digital Biomarker Data in Cardiovascular Risk Stratification Immanuel Simbolon; Cindy Susanti; Gayatri Putri; Karina Chandra; Muhammad Yoshandi; Daniel Hilman Maulana
Natural Sciences Engineering and Technology Journal Vol. 5 No. 2 (2025): Natural Sciences Engineering and Technology Journal
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/nasetjournal.v5i2.71

Abstract

Conventional models for stratifying cardiovascular disease (CVD) risk have limitations. The integration of static genomic data and dynamic digital biomarkers from wearable technology holds theoretical promise, but its potential quantitative impact remains poorly defined. This study aimed to develop and validate an in-silico framework to quantify the theoretical maximum predictive gain of an integrated risk model under idealized conditions. We developed a sophisticated data generating process (DGP) to create a synthetic dataset of 5,000 individuals. The DGP incorporated demographic and clinical variables with distributions and correlations based on epidemiological literature. It included a simulated polygenic risk score (PRS) for coronary artery disease and advanced digital biomarkers derived from wireless health monitoring data, such as heart rate variability (HRV) and time in moderate-to-vigorous physical activity (MVPA). The 10-year risk of Major Adverse Cardiovascular Events (MACE) was generated via a defined logistic function incorporating these variables plus stochastic noise. We compared the performance of the ACC/AHA Pooled Cohort Equations (PCE) against several machine learning models (Logistic Regression, Random Forest, XGBoost) using the area under the receiver operating characteristic curve (AUC-ROC), precision, recall, and F1-score. In this simulated environment, the integrated XGBoost model achieved near-optimal predictive performance with an AUC-ROC of 0.92 (95% CI, 0.90-0.94), significantly outperforming the benchmark PCE model (AUC-ROC 0.76; 95% CI, 0.73-0.79; p < 0.001). The inclusion of the PRS and, most notably, dynamic digital biomarkers like HRV, provided substantial incremental improvements in risk discrimination over traditional factors alone. In conclusion, this in-silico study demonstrates the substantial theoretical potential of integrating genomic and advanced digital biomarker data through machine learning for CVD risk stratification. While these idealized results are not directly generalizable, they provide a quantitative rationale for pursuing real-world data collection and validation studies. This work establishes a methodological proof-of-concept and highlights the potential for a paradigm shift toward more dynamic and personalized cardiovascular risk assessment.
The Future of the Firm: A Comparative Institutional Analysis of Transaction Costs in DAOs versus Traditional Corporations Benyamin Wongso; Caelin Damayanti; Muhammad Faiz; Anies Fatmawati; Aylin Yermekova; Delia Tamim; Dais Susilo; Danila Adi Sanjaya; Gayatri Putri
Enigma in Economics Vol. 3 No. 2 (2025): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v3i2.94

Abstract

The emergence of Decentralized Autonomous Organizations (DAOs) presents a fundamental challenge to the traditional corporate form, which has dominated economic organization for over a century. Built on blockchain technology, DAOs propose a new model for coordinating economic activity. This study addressed the critical question of institutional efficiency by applying the lens of Transaction Cost Economics (TCE) to compare DAOs and traditional corporations. A comparative institutional analysis was conducted using a mixed-methods approach. We employed a multiple case study design, analyzing two representative DAOs and two analogous traditional corporations from Q1 2023 to Q4 2024. Data collection involved the systematic analysis of archival records, including 215 DAO governance proposals and corporate filings, and 32 semi-structured interviews with key participants. A novel analytical framework was developed to categorize transaction costs into ex ante (search, bargaining) and ex post (monitoring, enforcement), further distinguishing between 'on-chain' and 'off-chain' costs. The study revealed significant trade-offs between the two organizational forms. Traditional corporations exhibited high ex ante bargaining costs (legal, negotiation) and ex post monitoring costs (managerial overhead), but benefited from established legal frameworks that reduced enforcement uncertainty. Conversely, DAOs significantly lowered specific transaction costs through automation via smart contracts, particularly in on-chain bargaining and enforcement for codified tasks. However, DAOs incurred substantial, often hidden, new transaction costs related to off-chain social coordination, governance participation, and navigating legal ambiguity. This was termed the 'Governance Overhead Paradox'. In conclusion, DAOs do not represent a universally superior organizational form but rather a new point on an institutional possibility frontier. They are highly efficient for tasks that are global, permissionless, and computationally verifiable. Traditional firms retain advantages in contexts requiring complex, subjective decision-making and legal certainty. The future of the firm is likely not a replacement of one form by the other, but a pluralistic ecosystem where hybrid models emerge.
Systemic Contagion or Digital Diversifier? A Dynamic Quantification of the Cryptocurrency Market's Evolving Role in Global Financial Risk Transmission Abdul Malik; Gayatri Putri; Hesti Putri; Ahmad Badruddin
Enigma in Economics Vol. 3 No. 2 (2025): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v3i2.99

Abstract

The proliferation of crypto-assets has raised critical questions about their impact on global financial stability. This study rigorously investigates the structural evolution of the cryptocurrency market's role within the global financial system, testing the hypothesis that it has transitioned from a peripheral, shock-absorbing entity into a systemically significant transmitter of financial risk. We employ a Time-Varying Parameter Vector Autoregression (TVP-VAR) model on daily data from January 1, 2017, to December 31, 2024, examining the dynamic connectedness between a bespoke, rebalanced cryptocurrency index (CRIX20) and key global financial indicators (S&P 500, MSCI World, VIX, DXY). The econometric framework utilizes a Bayesian estimation approach with standard priors, a 200-day rolling window, and a 10-day forecast horizon for Generalized Forecast Error Variance Decompositions (GFEVD). Methodological robustness is confirmed through structural break tests and sensitivity analysis of the forecast horizon. Our findings reveal a profound structural transformation. Prior to mid-2020, the cryptocurrency market was a consistent net receiver of financial spillovers. A structural break, formally identified in the third quarter of 2020, marks a definitive regime shift. Post-break, the crypto market has become a significant and persistent net transmitter of risk to the traditional financial system. The total connectedness index for the entire system shows a marked secular increase, with the crypto market's contribution to systemic risk growing substantially. Gross spillover analysis confirms this shift is driven by a dramatic increase in risk transmission from the crypto market to other assets. In conclusion, the cryptocurrency market can no longer be considered an isolated ecosystem; it is now an integral and potentially destabilizing component of the global financial architecture. The era of crypto-assets as reliable diversifiers has waned, replaced by a new reality where shocks originating within this market pose a credible threat to broader financial stability. These findings present urgent challenges for regulatory oversight, systemic risk monitoring, and portfolio management.
Synergistic Alpha: A Deep Learning Framework for Forecasting Cryptocurrency Returns by Fusing On-Chain, Sentiment, and Market Data Gayatri Putri; Sonia Vernanda; Anies Fatmawati; Muhammad Faiz
Enigma in Economics Vol. 3 No. 2 (2025): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v3i2.103

Abstract

The inherent volatility and unique economic characteristics of cryptocurrencies pose significant challenges to conventional asset-pricing models. This study investigates whether a synergistic fusion of the network’s fundamental data (on-chain metrics), market behavioral dynamics (social media sentiment), and historical market data can uncover statistically and economically significant predictive power when analyzed by advanced deep learning architectures. We developed a sophisticated forecasting and backtesting framework to predict the daily log returns of Bitcoin (BTC). The methodology is grounded in rigorous time-series analysis, beginning with Augmented Dickey-Fuller tests to ensure data stationarity. We constructed a multi-modal dataset from specified, high-frequency sources (Kaiko, Glassnode, and a custom-built FinBERT sentiment model) spanning January 1, 2018, to December 31, 2023. We systematically compared the performance of a state-of-the-art Transformer model against Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and robust econometric baselines, including GARCH(1,1) and ARIMA. The models were evaluated not only on statistical accuracy (such as Root Mean Squared Error and Directional Accuracy) but also on their economic significance via a realistic trading backtest that incorporates transaction costs. The fully integrated Hybrid Transformer model demonstrated superior forecasting accuracy, achieving the highest Directional Accuracy (61.25%). More importantly, in a transaction-cost-aware backtest, a trading strategy guided by this model yielded an annualized Sharpe Ratio of 1.58, significantly outperforming a buy-and-hold benchmark (Sharpe Ratio: 0.72). The strategy generated a statistically significant Jensen's Alpha of 0.18 (p < 0.01), indicating substantial risk-adjusted excess returns. Feature importance analysis via SHAP confirmed that social media sentiment and the NVT Signal were the most influential predictors beyond past returns. In conclusion, the findings provide strong evidence that the cryptocurrency market exhibits exploitable inefficiencies. The fusion of on-chain, sentiment, and market data, when processed by attention-based neural networks, uncovers a statistically and economically significant predictive edge. This work challenges the semi-strong form of market efficiency for digital assets and suggests that alpha is derivable from the complex, high-dimensional data footprints unique to this asset class, providing a robust framework for quantitative investment strategies.
Co-Authors Aaliyah El-Hussaini Abdul Malik Ahmad Badruddin Andi Fatihah Syahrir Anies Fatmawati Anita Havyasari Arya Ganendra Aylin Yermekova Benyamin Wongso Caelin Damayanti Cindy Susanti Dais Susilo Daniel Hilman Maulana Danila Adi Sanjaya Delia Tamim Evelyn Wang Firman Hadi Henrietta Noir Hesti Putri Iis Sugandhi Immanuel Simbolon Karina Chandra Muhammad Faiz Muhammad Faiz Muhammad Hasan Muhammad Yoshandi Nur Diana Sonia Vernanda Theresia Putri Sinaga